Photometric Redshift Measurements Research Articles

A Photometric Redshift Catalogue of Galaxies from the DESI Legacy Imaging Surveys DR10

The large-scale measurement of galaxy redshifts holds significant importance for cosmological research, as well as for understanding the formation and evolution of galaxies. This study utilizes a known sample obtained by cross-correlating the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys DR10 galaxy catalog with various galaxy catalogs from different spectroscopic surveys. The methods Easy and Accurate Photometric Redshifts from Yale (EAZY) and CatBoost are employed to estimate redshifts. In the case of EAZY, the known sample is used solely for testing, while CatBoost utilizes it for both training and testing purposes. The known sample is categorized into different subsamples based on various classification methods. Several CatBoost regression models are trained and optimized using these subsamples. By comparing the performance of different methods and models, it is observed that the two-step and two-part models outperform the one-step model, with further enhancements achieved through the combination of the two-step and two-part models. Based on the findings from all experiments, we propose a photometric redshift estimation workflow designed to facilitate the photometric redshift measurement of all galaxies within the DESI Legacy Imaging Surveys DR10. Consequently, a photometric redshift catalog has been released, comprising a total of 1,533,107,988 galaxies. Among these, 312,960,837 galaxies have reliable redshift estimates, determined using the CatBoost algorithm, with magnitude limits set at g > 24.0, r > 23.4, and z > 22.5. For galaxies with g, r, and z magnitudes exceeding these thresholds, the photometric redshifts estimated by EAZY can be employed as a reference.

UVCANDELS: Catalogs of Photometric Redshifts and Galaxy Physical Properties

The UltraViolet imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) program provides deep Hubble Space Telescope (HST) F275W and F435W imaging over four CANDELS fields (GOODS-N, GOODS-S, COSMOS, and Extended Groth Strip). We combine this newly acquired UV imaging with existing HST imaging from CANDELS as well as existing ancillary data to obtain robust photometric redshifts and reliable estimates for galaxy physical properties for over 150,000 galaxies in the ∼430 arcmin2 UVCANDELS area. Here, we leverage the power of the new UV photometry to not only improve the photometric redshift measurements in these fields, but also constrain the full redshift probability distribution combining multiple redshift-fitting tools. Furthermore, using the full UV-to-IR photometric data set, we measure the galaxy physical properties by fitting templates from population synthesis models with two different parameterizations (flexible and fixed form) of the star formation histories (SFHs). Compared to the flexible SFH parameterization, we find that the fixed-form SFHs systematically underestimate the galaxy stellar masses, both at the low-mass (≲109 M ⊙) and high-mass (≳1010 M ⊙) end, by as much as ∼0.5 dex. This underestimation is primarily due the limited ability of fixed-form SFH parameterization to simultaneously capture the chaotic nature of star formation in these galaxies.

The Ultraviolet Luminosity Function at 0.6 < z < 1 from UVCANDELS

UVCANDELS is a Hubble Space Telescope Cycle-26 Treasury Program awarded 164 orbits of primary ultraviolet (UV) F275W imaging and coordinated parallel optical F435W imaging in four CANDELS fields—GOODS-N, GOODS-S, EGS, and COSMOS—covering a total area of ∼426 arcmin2. This is ∼2.7 times larger than the area covered by previous deep-field space UV data combined, reaching a depth of about 27 and 28 ABmag (5σ in 0.”2 apertures) for F275W and F435W, respectively. Along with new photometric catalogs, we present an analysis of the rest-frame UV luminosity function (LF), relying on our UV-optimized aperture photometry method, yielding a factor of 1.5 increase over H-isophot aperture photometry in the signal-to-noise ratios of galaxies in our F275W imaging. Using well-tested photometric redshift measurements, we identify 5810 galaxies at redshifts 0.6 < z < 1, down to an absolute magnitude of M UV = −14.2. In order to minimize the effect of uncertainties in estimating the completeness function, especially at the faint end, we restrict our analysis to sources above 30% completeness, which provides a final sample of 4726 galaxies at −21.5 < M UV < −15.5. We performed a maximum likelihood estimate to derive the best-fit parameters of the UV LF. We report a best-fit faint-end slope of α=−1.359−0.041+0.041 at z ∼ 0.8. Creating subsamples at z ∼ 0.7 and z ∼ 0.9, we observe a possible evolution of α with redshift. The unobscured UV luminosity density at M UV < −10 is derived as ρUV=1.339−0.030+0.027(×1026ergs−1Hz−1Mpc−3) using our best-fit LF parameters. The new F275W and F435 photometric catalogs from UVCANDELS have been made publicly available on the Barbara A. Mikulski Archive for Space Telescopes.

Light fields during inflation from BOSS and future galaxy surveys

Primordial non-Gaussianity generated by additional fields present during inflation offers a compelling observational target for galaxy surveys. These fields are of significant theoretical interest since they offer a window into particle physics in the inflaton sector. They also violate the single-field consistency conditions and induce a scale-dependent bias in the galaxy power spectrum. In this paper, we explore this particular signal for light scalar fields and study the prospects for measuring it with galaxy surveys. We find that the sensitivities of current and future surveys are remarkably stable for different configurations, including between spectroscopic and photometric redshift measurements. This is even the case at non-zero masses where the signal is not obviously localized on large scales. For realistic galaxy number densities, we demonstrate that the redshift range and galaxy bias of the sample have the largest impact on the sensitivity in the power spectrum. These results additionally motivated us to explore the potentially enhanced sensitivity of Vera Rubin Observatory's LSST through multi-tracer analyses. Finally, we apply this understanding to current data from the last data release of the Baryon Oscillation Spectroscopic Survey (BOSS DR12) and place new constraints on light fields coupled to the inflaton.

Cosmology and Astrophysics with Standard Sirens and Galaxy Catalogs in View of Future Gravitational Wave Observations

With the growing number of gravitational wave (GW) detections and the advent of large galaxy redshift surveys, a new era in cosmology is emerging. This study explores the synergies between GWs and galaxy surveys to jointly constrain cosmological and GW population parameters. We introduce CHIMERA, a novel code for GW cosmology combining information from the population properties of compact binary mergers and galaxy catalogs. We study constraints for scenarios representative of the LIGO-Virgo-KAGRA O4 and O5 observing runs, assuming to have a complete catalog of potential host galaxies with either spectroscopic or photometric redshift measurements. We find that a percent-level measurement of H 0 could be achieved with the best 100 binary black holes (BBHs) in O5 using a spectroscopic galaxy catalog. In this case, the intrinsic correlation that exists between H 0 and the BBH population mass scales is broken. Instead, by using a photometric catalog the accuracy is degraded up to a factor of ∼9, leaving a significant correlation between H 0 and the mass scales that must be carefully modeled to avoid bias. Interestingly, we find that using spectroscopic redshift measurements in the O4 configuration yields a better constraint on H 0 compared to the O5 configuration with photometric measurements. In view of the wealth of GW data that will be available in the future, we argue the importance of obtaining spectroscopic galaxy catalogs to maximize the scientific return of GW cosmology.

YOLO–CL: Galaxy cluster detection in the SDSS with deep machine learning

Galaxy clusters are powerful probes for cosmological models. Next-generation, large-scale optical and infrared surveys are poised to reach unprecedented depths and, thus, they require highly complete and pure cluster catalogs, with a well-defined selection function. We have developed a new cluster detection algorithm named YOLO for CLuster detection (YOLO–CL), which is a modified version of the state-of-the-art object detection deep convolutional network named You only look once (YOLO) that has been optimized for the detection of galaxy clusters. We trained YOLO–CL on the red-sequence Matched-filter Probabilistic Percolation (redMaPPer) cluster catalog, based on Sloan Digital Sky Survey (SDSS) color images. We find that YOLO–CL detects 95–98% of the redMaPPer clusters, with a purity of 95–98%, that is calculated by applying the network to SDSS blank fields. When compared to the Meta-Catalog of X-Ray Detected Clusters of Galaxies 2021 (MCXC2021) X-ray catalog in the SDSS footprint, YOLO–CL recovers all clusters at LX ≳ 2–3 × 1044 erg s−1, M500 ≳ 2–3 × 1014M⊙, R500≳0.75–0.8 Mpc and 0.4 ≲ z ≲ 0.6. When compared to the redMaPPer detection of the same MCXC2021 clusters, we find that YOLO–CL is more complete than redMaPPer, which means that the neural network has indeed improved the cluster detection efficiency of its training sample. In fact, YOLO–CL detects ~98% of the MCXC2021 clusters with an X-ray surface brightness of IX,500 ≳ 20 × 10−15 erg s−1 cm−2 arcmin−2 at 0.2 ≲ z ≲ 0.6 and ~100% of the MCXC2021 clusters with IX,500 ≳ 30 × 10−15 erg s−1 cm−2 arcmin−2 at 0.3 ≲ z ≲ 0.6; while redMaPPer detects ~98% of the MCXC2021 clusters with IX,500 ≳ 55 × 10−15 erg s−1 cm−2 arcmin−2 at 0.2 ≲ z ≲ 0.6 and ~100% of the MCXC2021 clusters with IX,500 ≳ 20 × 10−15 erg s−1 cm−2 arcmin−2 at 0.5 ≲ z ≲ 0.6. The YOLO–CL selection function is approximately constant with redshift, with respect to the MCXC2021 cluster X-ray surface brightness. YOLO–CL exhibits a high level of performance when compared to traditional detection algorithms applied to SDSS. Deep learning networks display a strong advantage over traditional galaxy cluster detection techniques because they do not require the galaxy’s photometric and photometric redshift catalogs. This eliminates systematic uncertainties that may be introduced during source detections and photometry, as well as photometric redshift measurements. Our results show that YOLO–CL is an efficient alternative to traditional cluster detection methods. In general, this work shows that it is worth exploring the performance of deep convolution networks for future cosmological cluster surveys, such as the Rubin/Legacy Survey of Space and Time (Rubin/LSST), Euclid, and Roman Space Telescope surveys.

Potential scientific synergies in weak lensing studies between the CSST andEuclidspace probes

Aims.With the next generation of large surveys poised to join the ranks of observational cosmology in the near future, it is important to explore their potential synergies and to maximize their scientific outcomes. In this study, we aim to investigate the complementarity of two upcoming space missions:Euclidand the China Space Station Telescope (CSST), both of which will be focused on weak gravitational lensing for cosmology. In particular, we analyze the photometric redshift (photo-z) measurements by combining NUV,u, g, r, i, z, ybands from CSST with the VIS,Y, J, Hbands fromEuclid, and other optical bands from the ground-basedVera C. RubinObservatory Legacy Survey of Space and Time (LSST) and Dark Energy Survey. We also consider the advantages of combining the two space observational data in simplifying image deblending. ForEuclid, weak lensing measurements use the broad optical wavelength range of 550−900 nm, for which chromatic point-spread function (PSF) effects are significant. For this purpose, the CSST narrow-band data in the optical can provide valuable information forEuclidto obtain more accurate PSF measurements and to calibrate the color and color-gradient biases for galaxy shear measurements.Methods.We created image simulations, using theHubbleDeep UV data as the input catalog, for different surveys and quantified the photo-zperformance using theEAZYtemplate fitting code. For the blending analyses, we employed high-resolution HST-ACS CANDELSF606WandF814Wdata to synthesize mock simulated data forEuclid, CSST, and an LSST-like survey. We analyzed the blending fraction for different cases as well as the blending effects on galaxy photometric measurements. Furthermore, we demonstrated that CSST can provide a large enough number of high signal-to-noise ratio multi-band galaxy images to calibrate the color-gradient biases forEuclid.Results.The sky coverage ofEuclidlies entirely within the CSST footprint. The combination ofEuclidwith the CSST data can thus be done more uniformly than with the various ground-based data that are part of theEuclidsurvey. Our studies show that by combiningEuclidand CSST, we can reach a photo-zprecision ofσNMAD ≈ 0.04 and an outlier fraction ofη ≈ 2.4% at the nominal depth of theEuclidWide Survey (VIS &lt; 24.5 AB mag). For CSST, including theEuclidY, J, Hbands reduces the overall photo-zoutlier fraction from ∼8.5% to 2.4%. Forz &gt; 1, the improvements are even more significant. Because of the similarly high resolutions, the data combination ofEuclidand CSST can be relatively straightforward for photometry measurements. On the other hand, to include ground-based data, sophisticated deblending utilizing priors from high-resolution space observations are required. The multi-band data from CSST are very helpful in controlling the chromatic PSF effect forEuclidVIS shear measurements. The color-gradient bias forEuclidgalaxies with different bulge-to-total flux ratio at different redshifts can be well calibrated to the level of 0.1% using galaxies from the CSST deep survey.

Forecasts for cosmological measurements based on the angular power spectra of AGN and clusters of galaxies in the SRG/eROSITA all-sky survey

Context. The eROSITA X-ray telescope aboard the Spectrum-Roentgen-Gamma (SRG) orbital observatory, in the course of its all-sky survey, is expected to detect about three million active galactic nuclei (AGNs) and approximately one hundred thousand clusters and groups of galaxies. Such a sample, clean and uniform, complemented with redshift information, will open a new window into the studies of the large-scale structure (LSS) of the Universe and the determination of its cosmological parameters. Aims. The purpose of this work is to assess the prospects of cosmological measurements with the eROSITA sample of AGNs and clusters of galaxies. We assumed the availability of photometric redshift measurements for eROSITA sources and explored the impact of their quality on our forecasts. Methods. As the LSS probe, we use the redshift-resolved angular power spectrum of the density fluctuations of objects. We employed a Fisher-matrix formalism and assumed flat ΛCDM cosmology to forecast the constraining power of eROSITA samples of AGNs and clusters of galaxies. We computed the LSS-relevant characteristics of AGNs and clusters in the framework of the halo model and their X-ray luminosity functions. As the baseline scenario, we considered the full four-year all-sky survey and investigated the impact of reducing the survey length to two years. Results. We find that the accuracy of photometric redshift estimates has a more profound effect on cosmological measurements than the fraction of catastrophic errors. Under realistic assumptions about the photometric redshift quality, the marginalised errors on the cosmological parameters achieve 1 − 10% accuracy depending on the cosmological priors used from other experiments. The statistical significance of Baryon acoustic oscillation detection in angular power spectra of AGNs and clusters of galaxies considered individually achieves 5 − 6σ. Our results demonstrate that the eROSITA sample of AGNs and clusters of galaxies used in combination with currently available photometric redshift estimates will provide cosmological constraints on a par with dedicated optical LSS surveys.

Statistical strong lensing

Context. Strong lensing mass measurements require the knowledge of the redshift of both the lens and the source galaxy. Traditionally, spectroscopic redshifts are used for this purpose. Upcoming surveys, however, will lead to the discovery of ∼105 strong lenses, and it will be very difficult to obtain spectroscopic redshifts for most of them. Photometric redshift measurements will also be very challenging due to the blending between lens and source light. Aims. The goal of this work is to demonstrate how to carry out an inference of the structural properties of the galaxy population from the analysis of a set of strong lenses with no individual source redshift measurements, and to assess the loss in precision compared to the case in which spectroscopic redshifts are available. Methods. Building on the formalism introduced in Paper III, I developed a method that allows a statistical strong lensing inference to be carried out while marginalising over the source redshifts. This method, which relies on the knowledge of the properties of the unlensed background source population and of the selection function of the survey, generalises an approach known as photogeometric redshift, originally introduced by the Strong Lensing Legacy Survey collaboration. I tested the method on simulated data consisting of a subset of 137 strong lenses that is complete above a cut in observational space. Results. The method recovers the properties of the galaxy population with a precision that is comparable to that attainable in the case in which individual source redshifts are known. Conclusions. The photogeometric redshift method is a viable approach for the analysis of large sets of strong lenses provided that the background source population properties and lens selection function are well known.

Calibrating Photometric Redshift Measurements with the Multi-channel Imager (MCI) of the China Space Station Telescope (CSST)

The China Space Station Telescope (CSST) photometric survey aims to perform a high spatial resolution (∼0.″15) photometric imaging for the targets that cover a large sky area (∼17,500 deg2) and wide wavelength range (from NUV to NIR). It expects to explore the properties of dark matter, dark energy, and other important cosmological and astronomical areas. In this work, we evaluate whether the filter design of the Multi-channel Imager (MCI), one of the five instruments of the CSST, can provide accurate photometric redshift (photoz) measurements with its nine medium-band filters to meet the relevant scientific objectives. We generate the mock data based on the COSMOS photometric redshift catalog with astrophysical and instrumental effects. The application of upper limit information of low signal-to-noise ratio data is adopted in the estimation of photoz. We investigate the dependency of photoz accuracy on the filter parameters, such as band position and width. We find that the current MCI filter design can achieve good photoz measurements with accuracy σ z ≃ 0.017 and outlier fraction f c ≃ 2.2%. It can effectively improve the photoz measurements of the main CSST survey using the Survey Camera to an accuracy σ z ≃ 0.015 and outlier fraction f c ≃ 1.5%. This indicates that the original MCI filters are proper for the photoz calibration.

Euclidpreparation

Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-zs at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2 − 2.6 redshift range that theEuclidmission will probe. We designed a challenge using emulatedEucliddata drawn from three photometric surveys of the COSMOS field. The data was divided into two samples: one calibration sample for which photometry and redshifts were provided to the participants; and the validation sample, containing only the photometry to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates the sources they consider unfit for use in cosmological analyses. The performance of each method was assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, that is to say sources for which the photo-zdeviates by more than 0.15(1 + z) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. We find that no machine-learning method provides good results in the regions of galaxy color-space that are sparsely populated by spectroscopic-redshifts, for examplez &gt; 1. However they generally perform better than template-fitting methods at low redshift (z &lt; 0.7), indicating that template-fitting methods do not use all of the information contained in the photometry. We introduce metrics that quantify both photo-zprecision and completeness of the samples (post-rejection), since both contribute to the final figure of merit of the science goals of the survey (e.g., cosmic shear fromEuclid). Template-fitting methods provide the best results in these metrics, but we show that a combination of template-fitting results and machine-learning results with rejection criteria can outperform any individual method. On this basis, we argue that further work in identifying how to best select between machine-learning and template-fitting approaches for each individual galaxy should be pursued as a priority.

MIFAL: fully automated Multiple-Image Finder ALgorithm for strong-lens modelling – proof of concept

ABSTRACT We outline a simple procedure designed for automatically finding sets of multiple images in strong lensing (SL) clusters. We show that by combining (a) an arc-finding (or source extracting) program, (b) photometric redshift measurements, and (c) a preliminary light-traces-mass lens model, multiple-image systems can be identified in a fully automated (‘blind’) manner. The presented procedure yields an assessment of the likelihood of each arc to belong to one of the multiple-image systems, as well as the preferred redshift for the different systems. These could be then used to automatically constrain and refine the initial lens model for an accurate mass distribution. We apply this procedure to Cluster Lensing And Supernova with Hubble observations of three galaxy clusters, MACS J0329.6-0211, MACS J1720.2 + 3536, and MACS J1931.8-2635, comparing the results to published SL analyses where multiple images were verified by eye on a particular basis. In the first cluster all originally identified systems are recovered by the automated procedure, and in the second and third clusters about half are recovered. Other known systems are not picked up, in part due to a crude choice of parameters, ambiguous photometric redshifts, or inaccuracy of the initial lens model. On top of real systems recovered, some false images are also mistakenly identified by the procedure, depending on the thresholds used. While further improvements to the procedure and a more thorough scrutinization of its performance are warranted, the work constitutes another important step toward fully automatizing SL analyses for studying mass distributions of large cluster samples.

More connected, more active: galaxy clusters and groups at z ∼ 1 and the connection between their quiescent galaxy fractions and large-scale environments

ABSTRACT High-redshift galaxy clusters, unlike local counterparts, show diverse star formation activities. However, it is still unclear what keeps some of the high-redshift clusters active in star formation. To address this issue, we performed a multiobject spectroscopic observation of 226 high-redshift (0.8 &lt; z &lt; 1.3) galaxies in galaxy cluster candidates and the areas surrounding them. Our spectroscopic observation reveals six to eight clusters/groups at z ∼ 0.9 and z ∼ 1.3. The redshift measurements demonstrate the reliability of our photometric redshift measurements, which in turn gives credibility for using photometric redshift members for the analysis of large-scale structures (LSSs). Our investigation of the large-scale environment (∼10 Mpc) surrounding each galaxy cluster reveals LSSs – structures up to ∼10 Mpc scale – around many of, but not all, the confirmed overdensities and the cluster candidates. We investigate the correlation between quiescent galaxy fraction of galaxy overdensities and their surrounding LSSs, with a larger sample of ∼20 overdensities including photometrically selected overdensities at 0.6 &lt; z &lt; 0.9. Interestingly, galaxy overdensities embedded within these extended LSSs show a lower fraction of quiescent galaxies ($\sim 20{{\ \rm per\ cent}}$) than isolated ones at similar redshifts (with a quiescent galaxy fraction of $\sim 50 {{\ \rm per\ cent}}$). Furthermore, we find a possible indication that clusters/groups with a high quiescent galaxy fraction are more centrally concentrated. Based on these results, we suggest that LSSs are the main reservoirs of gas and star-forming galaxies to keep galaxy clusters fresh and extended in size at z ∼ 1.

Accurate Modeling of the Projected Galaxy Clustering in Photometric Surveys. I. Tests with Mock Catalogs

Abstract We develop a novel method to explore the galaxy–halo connection using the galaxy imaging surveys by modeling the projected two-point correlation function measured from the galaxies with reasonable photometric redshift measurements. By assuming a Gaussian form of the photometric redshift errors, we are able to simultaneously constrain the halo occupation distribution (HOD) models and the effective photometric redshift uncertainties. Tests with mock galaxy catalogs demonstrate that this method can successfully recover (within ∼1σ) the intrinsic large-scale galaxy bias, as well as the HOD models and the effective photometric redshift uncertainty. This method also works well even for galaxy samples with 10% catastrophic photometric redshift errors.

J-PAS: forecasts on interacting dark energy from baryon acoustic oscillations and redshift-space distortions

ABSTRACT We estimate the constraining power of Javalambre-Physics of the Accelerated Universe Astrophysical Survey (J-PAS) for parameters of an interacting dark energy (DE) cosmology. The survey is expected to map several millions of luminous red galaxies, emission line galaxies, and quasars in an area of thousands of square degrees in the northern sky with precise photometric redshift measurements. Forecasts for the DESI and Euclid surveys are also evaluated and compared to J-PAS. Using the Fisher matrix approach, we find that J-PAS can place constraints on the interaction parameter comparable to those from DESI, with an absolute uncertainty of about 0.02, when the interaction term is proportional to the dark matter energy density, and almost as good, of about 0.01, when the interaction is proportional to the DE density. For the equation of state of DE, the constraints from J-PAS are slightly better in the two cases (uncertainties 0.04–0.05 against 0.05–0.07 around the fiducial value −1). Both surveys stay behind Euclid but follow it closely, imposing comparable constraints in all specific cases considered.

Towards emulating cosmic shear data: revisiting the calibration of the shear measurements for the Kilo-Degree Survey

Exploiting the full statistical power of future cosmic shear surveys will necessitate improvements to the accuracy with which the gravitational lensing signal is measured. We present a framework for calibrating shear with image simulations that demonstrates the importance of including realistic correlations between galaxy morphology, size, and more importantly, photometric redshifts. This realism is essential to ensure that selection and shape measurement biases can be calibrated accurately for a tomographic cosmic shear analysis. We emulate Kilo-Degree Survey (KiDS) observations of the COSMOS field using morphological information fromHubbleSpace Telescope imaging, faithfully reproducing the measured galaxy properties from KiDS observations of the same field. We calibrate our shear measurements fromlensfit, and find through a range of sensitivity tests thatlensfit is robust and unbiased within the allowed two per cent tolerance of our study. Our results show that the calibration has to be performed by selecting the tomographic samples in the simulations, consistent with the actual cosmic shear analysis, because the joint distributions of galaxy properties are found to vary with redshift. Ignoring this redshift variation could result in misestimating the shear bias by an amount that exceeds the allowed tolerance. To improve the calibration for future cosmic shear analyses, it will also be essential to correctly account for the measurement of photometric redshifts, which requires simulating multi-band observations.

Testing photometric redshift measurements with filter definition of the Chinese Space Station Optical Survey (CSS-OS)

The Chinese Space Station Optical Survey (CSS-OS) is a major science project of the Space Application System of the China Manned Space Program. This survey is planned to perform both photometric imaging and slitless spectroscopic observations, and it will focus on different cosmological and astronomical goals. Most of these goals are tightly dependent on the accuracy of photometric redshift (photo-z) measurement, especially for the weak gravitational lensing survey as a main science driver. In this work, we assess if the current filter definition can provide accurate photo-z measurement to meet the science requirement. We use the COSMOS galaxy catalog to create a mock catalog for the CSS-OS. We compare different photo-z codes and fitting methods that using the spectral energy distribution (SED) template-fitting technique, and choose to use a modified LePhare code in photo-z fitting process. Then we investigate the CSS-OS photo-z accuracy in certain ranges of filter parameters, such as band position, width, and slope. We find that the current CSS-OS filter definition can achieve reasonably good photo-z results with sigma_z~0.02 and outlier fraction ~3%.

Galaxy tagging: photometric redshift refinement and group richness enhancement

We present a new scheme, $\it{galtag}$, for refining the photometric redshift measurements of faint galaxies by probabilistically tagging them to observed galaxy groups constructed from a brighter, magnitude-limited spectroscopy survey. First, this method is tested on the DESI light-cone data constructed on the GALFORM galaxy formation model to tests its validity. We then apply it to the photometric observations of galaxies in the Kilo-Degree Imaging Survey (KiDS) over a 1 deg$^2$ region centred at 15$^\mathrm{h}$. This region contains Galaxy and Mass Assembly (GAMA) deep spectroscopic observations (i-band<22) and an accompanying group catalogue to r-band<19.8. We demonstrate that even with some trade-off in sample size, an order of magnitude improvement on the accuracy of photometric redshifts is achievable when using $\it{galtag}$. This approach provides both refined photometric redshift measurements and group richness enhancement. In combination these products will hugely improve the scientific potential of both photometric and spectroscopic datasets. The $\it{galtag}$ software will be made publicly available at https://github.com/pkaf/galtag.git.

Calibrating photometric redshifts with intensity mapping observations

Imaging surveys of galaxies will have a high number density and angular resolution yet a poor redshift precision. Intensity maps of neutral hydrogen (HI) will have accurate redshift resolution yet will not resolve individual sources. Using this complementarity, we show how the clustering redshifts approach, proposed for spectroscopic surveys can also be used in combination with intensity mapping observations to calibrate the redshift distribution of galaxies in an imaging survey and, as a result, reduce uncertainties in photometric redshift measurements. We show how the intensity mapping surveys to be carried out with the MeerKAT, HIRAX and SKA instruments can improve photometric redshift uncertainties to well below the requirements of DES and LSST. The effectiveness of this method as a function of instrumental parameters, foreground subtraction and other potential systematic errors is discussed in detail.

The third data release of the Kilo-Degree Survey and associated data products

The Kilo-Degree Survey (KiDS) is an ongoing optical wide-field imaging survey with the OmegaCAM camera at the VLT Survey Telescope. It aims to image 1500 square degrees in four filters (ugri). The core science driver is mapping the large-scale matter distribution in the Universe, using weak lensing shear and photometric redshift measurements. Further science cases include galaxy evolution, Milky Way structure, detection of high-redshift clusters, and finding rare sources such as strong lenses and quasars. Here we present the third public data release (DR3) and several associated data products, adding further area, homogenized photometric calibration, photometric redshifts and weak lensing shear measurements to the first two releases. A dedicated pipeline embedded in the Astro-WISE information system is used for the production of the main release. Modifications with respect to earlier releases are described in detail. Photometric redshifts have been derived using both Bayesian template fitting, and machine-learning techniques. For the weak lensing measurements, optimized procedures based on the THELI data reduction and lensfit shear measurement packages are used. In DR3 stacked ugri images, weight maps, masks, and source lists for 292 new survey tiles (~300 sq.deg) are made available. The multi-band catalogue, including homogenized photometry and photometric redshifts, covers the combined DR1, DR2 and DR3 footprint of 440 survey tiles (447 sq.deg). Limiting magnitudes are typically 24.3, 25.1, 24.9, 23.8 (5 sigma in a 2 arcsec aperture) in ugri, respectively, and the typical r-band PSF size is less than 0.7 arcsec. The photometric homogenization scheme ensures accurate colors and an absolute calibration stable to ~2% for gri and ~3% in u. Separately released are a weak lensing shear catalogue and photometric redshifts based on two different machine-learning techniques.